"Smallytics™" which makes "Causal Discovery" easy to use, “CKE-LLM” (Causal Knowledge Extraction LLM) which improves its reliability.

Discovering the structure of causal relationships from data is called "Causal Discovery," and estimating causal effects is called "Causal Inference." When the structure of the causal relationship is known, "Causal Inference" can be conducted promptly. However, in many cases, uncovering unknown relationships drives innovation. In such instances, "Causal Discovery" proves to be a valuable tool. Yet, until now, this approach has been impractical due to the substantial increase in processing time required to compute all data combinations when estimating causal structures. With xCausal™, our Smallytics™ algorithm recommends variables that are causally related to the data you are analyzing from a variety of time series data combinations. This allows users to select and narrow down meaningful variables, and can drastically reduce the amount of combinatorial computation. Causal graphs with network structure can be generated instantly in tens of seconds to minutes. Therefore, users can iterate through trial and error with data to understand cause-and-effect relationships and formulate high-resolution hypotheses for solutions. Users can select from three types of causal discovery algorithms: Score based, constraint based, and hybrid.

In addition, "Causal Discovery" has the weakness that the calculated causal structure is not always correct, regardless of the type of algorithm. Therefore, it is known that pre-setting a causal relationship that is already known prior to the "Causal Discovery" improves the level of confidence. VELDT has developed CKE- LLM (Causal Knowledge Extraction LLM), a technology for extracting causal knowledge using LLM (Large Language Models), based on officially obtained reliable information sources. Extraction results are provided along with a process for people to review and decide whether to use them.

Future-oriented SaaS with highly scalable functionality, Easy to use academic know-how.

"xCausal™" is a SaaS that can be used by uploading data. No difficult programming is required, and everything can be used with a mouse-over or click. Knowledge of the problem you are trying to solve is necessary, but not necessarily as a data scientist. You can also use the CSV checker when uploading data to avoid redoing the process due to incomplete data. After uploading, you can easily complete missing data or create causality rules. Another unique aspect of this tool is that it provides functions that tend to be difficult with an easy-to-understand and sophisticated UI. xCausal™” makes Causal Discovery and Causal Inference quick and seamless, making it easier to use the know-how that has been used in the academic realm up until now.

It is also a future-oriented solution in that, unlike software installed on a PC or used in a client-server format, it is scalable, collaborative in its data analysis, and able to provide ongoing updates and additions of ever-evolving Causal AI functions.

Customization, Accompaniment Professional Services and Partnerships.

Depending on how "Causal Discovery" and "Causal Inference" are utilized, arriving at a viable solution may be challenging. Specifically, without obtaining the data potentially causing the issue, identifying a causal relationship becomes improbable. We provide professional services including consulting, data analysis, and customization to resolve these customer issues. Our advisory committee, comprising professors and associate professors from leading universities and research institutions, conducts cutting-edge research in various data science areas, including causal inference, machine learning, statistical analysis, and LLM, continuously generating new knowledge. Apart from customizing xCausal™ functionality, the system also facilitates diverse development avenues, such as applying outputs from causal models, integrating them with predictive models, and utilizing them alongside generative AI. Moreover, we aim to enhance our contribution to resolving client issues by collaborating with partner companies possessing expertise across diverse industries and domains in the near future.